1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
26/4/2025 Comida 55874 Tami Supermercado
28/4/2025 Comida 13050 Tami Cervezas MUT
29/4/2025 Electricidad 52507 Andrés enel
29/4/2025 Diosi 11990 Andrés arena 7kg superzoo
3/5/2025 Agua 17072 Andrés aguas andina
13/5/2025 VTR 22000 Andrés NA
17/5/2025 Electricidad 52404 Andrés NA
13/6/2025 VTR 22000 Andrés NA
22/6/2025 Electricidad 52401 Andrés NA
27/7/2025 Electricidad 52000 Andrés NA
27/7/2025 Comida 59147 Tami Supermercado
29/7/2025 Comida 10000 Andrés complemento comida
29/7/2025 Electrodomésticos/mantención casa 68000 Andrés NA
30/7/2025 Comida 24140 Tami Supermercado
31/7/2025 Gas 19100 Andrés NA
3/8/2025 Comida 86089 Tami Supermercado
10/8/2025 Comida 108649 Tami Supermercado
12/8/2025 Enceres 13490 Tami Confort
16/8/2025 VTR 22000 Andrés NA
17/8/2025 Comida 72586 Tami Supermercado
20/8/2025 Electricidad 65242 Andrés 49393- 42306 -47872= 1521
25/8/2025 Comida 35500 Andrés ida al super
25/8/2025 Comida 35000 Andrés piwen
27/8/2025 Comida 37314 Tami Supermercado
30/8/2025 Comida 67203 Tami Supermercado
31/8/2025 Diosi 28000 Andrés american litter
1/9/2025 Comida 7800 Andrés piwen
7/9/2025 Comida 93538 Tami Supermercado
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df  F value Pr(>F)    
## interrupt_var 1.0224e+09   2    5.312 0.0051 ** 
## lag_depvar    2.6333e+11   1 2736.419 <2e-16 ***
## Residuals     8.2279e+10 855                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -1735.885 16193.56 0.1412403
## 2-0 31330.491 23269.104 39391.88 0.0000000
## 2-1 24101.652 19442.059 28761.25 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
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## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 791  50018.43             2   49553.86
## 792  43772.86             2   50018.43
## 793  39235.43             2   43772.86
## 794  39905.00             2   39235.43
## 795  40374.43             2   39905.00
## 796  34230.57             2   40374.43
## 797  34324.14             2   34230.57
## 798  33491.57             2   34324.14
## 799  33366.43             2   33491.57
## 800  46646.86             2   33366.43
## 801  49770.86             2   46646.86
## 802  57339.86             2   49770.86
## 803  59799.14             2   57339.86
## 804  53577.14             2   59799.14
## 805  61775.29             2   53577.14
## 806  70627.86             2   61775.29
## 807  57888.43             2   70627.86
## 808  49960.71             2   57888.43
## 809  42923.71             2   49960.71
## 810  47284.86             2   42923.71
## 811  52284.86             2   47284.86
## 812  50191.00             2   52284.86
## 813  36465.86             2   50191.00
## 814  34525.14             2   36465.86
## 815  43199.14             2   34525.14
## 816  52757.43             2   43199.14
## 817  43200.86             2   52757.43
## 818  36772.29             2   43200.86
## 819  29568.00             2   36772.29
## 820  42362.00             2   29568.00
## 821  42566.29             2   42362.00
## 822  39596.00             2   42566.29
## 823  32925.00             2   39596.00
## 824  43416.57             2   32925.00
## 825  52624.86             2   43416.57
## 826  57733.71             2   52624.86
## 827  54120.57             2   57733.71
## 828  53353.43             2   54120.57
## 829  56286.86             2   53353.43
## 830  60626.86             2   56286.86
## 831  61375.29             2   60626.86
## 832  53710.86             2   61375.29
## 833  55795.57             2   53710.86
## 834  55130.14             2   55795.57
## 835  57700.14             2   55130.14
## 836  61333.14             2   57700.14
## 837  59230.71             2   61333.14
## 838  49195.00             2   59230.71
## 839  55436.43             2   49195.00
## 840  50353.14             2   55436.43
## 841  43194.86             2   50353.14
## 842  47539.71             2   43194.86
## 843  35271.00             2   47539.71
## 844  34774.86             2   35271.00
## 845  48788.71             2   34774.86
## 846  50717.71             2   48788.71
## 847  51727.43             2   50717.71
## 848  51313.14             2   51727.43
## 849  56125.29             2   51313.14
## 850  68503.71             2   56125.29
## 851  62945.00             2   68503.71
## 852  50209.71             2   62945.00
## 853  49436.29             2   50209.71
## 854  55308.00             2   49436.29
## 855  62650.86             2   55308.00
## 856  55683.00             2   62650.86
## 857  49762.14             2   55683.00
## 858  51835.00             2   49762.14
## 859  49806.43             2   51835.00
## 860  52799.57             2   49806.43
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   703 53564.75 21818.063
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86  50018.43  43772.86
## [792]  39235.43  39905.00  40374.43  34230.57  34324.14  33491.57  33366.43
## [799]  46646.86  49770.86  57339.86  59799.14  53577.14  61775.29  70627.86
## [806]  57888.43  49960.71  42923.71  47284.86  52284.86  50191.00  36465.86
## [813]  34525.14  43199.14  52757.43  43200.86  36772.29  29568.00  42362.00
## [820]  42566.29  39596.00  32925.00  43416.57  52624.86  57733.71  54120.57
## [827]  53353.43  56286.86  60626.86  61375.29  53710.86  55795.57  55130.14
## [834]  57700.14  61333.14  59230.71  49195.00  55436.43  50353.14  43194.86
## [841]  47539.71  35271.00  34774.86  48788.71  50717.71  51727.43  51313.14
## [848]  56125.29  68503.71  62945.00  50209.71  49436.29  55308.00  62650.86
## [855]  55683.00  49762.14  51835.00  49806.43  52799.57
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [852] 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2013.397412   4038.019974   -535.702244   2440.125821  -2964.945956 
##             7             8             9            10            11 
##    520.449201  -5653.344202  -1190.524905  -3969.134683   -423.840444 
##            12            13            14            15            16 
##  -4944.738162  -1617.958804   -908.229762    369.746603  -3248.729236 
##            17            18            19            20            21 
##   -385.553139  -2136.605945   6596.972954  -1528.849871  -1208.960745 
##            22            23            24            25            26 
##   1474.371468  -1185.822102    234.697092   1695.769617  -7099.261049 
##            27            28            29            30            31 
##    943.898611   8190.725379    425.147450     -6.893761  -2393.762313 
##            32            33            34            35            36 
##   1580.518434   4578.395391   1137.033395   2402.006360  -1855.850628 
##            37            38            39            40            41 
##   4617.432789   4309.363632  -2266.368253  -2975.346235  -1107.569158 
##            42            43            44            45            46 
## -10740.189282   7280.366890   2556.297769   1368.459720   8107.911841 
##            47            48            49            50            51 
##    696.256870   6538.302738   6728.963757  -5863.193537  -4784.179703 
##            52            53            54            55            56 
##  -5054.334393  -7928.549707   6122.420782  -4077.254288  -4898.686475 
##            57            58            59            60            61 
##   3847.421311    884.156159    -34.468025    140.168159  -4998.064982 
##            62            63            64            65            66 
##  18120.686993   3652.447067  -3632.302641   5934.103230   7357.393923 
##            67            68            69            70            71 
##  14657.640935   1723.593048 -13184.353327  -1293.528564   4653.493554 
##            72            73            74            75            76 
##  -4887.048584  -4397.378218 -10495.105883   2459.477667  -5403.644975 
##            77            78            79            80            81 
##   1055.648235  -6872.167536    535.689184  -2363.687726  -2703.780494 
##            82            83            84            85            86 
##  -3942.793315   -550.429705   2303.025233   3754.764105    473.431966 
##            87            88            89            90            91 
##   -487.138291    193.921173   4299.783368  -1160.978121   1151.610726 
##            92            93            94            95            96 
##  -2062.722649  -1044.584028    176.438150    273.993805  -7484.430395 
##            97            98            99           100           101 
##   2384.821783  -8606.280765  -2951.313868  -4051.146214  -1750.171226 
##           102           103           104           105           106 
##  -1274.278501   3168.835333  -2349.651700   2585.450522  -1163.488050 
##           107           108           109           110           111 
##    965.100852   2583.361967  -3155.284293  -4726.530608   -857.343117 
##           112           113           114           115           116 
##   1896.723775  11689.403371  -1235.801837   2673.446294   4269.609697 
##           117           118           119           120           121 
##   3512.503023  -1088.104992  -4706.808615  -3719.790107   2320.404180 
##           122           123           124           125           126 
##  -1729.883608   1341.464907   8860.494186    857.064452    140.051926 
##           127           128           129           130           131 
##  -2512.612466   2660.540079   7059.801534   1025.197377  -8487.225374 
##           132           133           134           135           136 
##   1752.420361   4139.987235  -3156.270498  -1415.714772   -851.610565 
##           137           138           139           140           141 
##  -3878.576204   1180.988988   -496.105512  -2914.475787   1714.940536 
##           142           143           144           145           146 
##  -1882.272109  -7831.905680   2030.369652  -3485.767457   2093.929776 
##           147           148           149           150           151 
##   -262.834584   1018.148333   -362.634094   1348.996119   1185.062049 
##           152           153           154           155           156 
##   3356.285979  -4859.043693  -1176.299973  -3238.377353   5951.691074 
##           157           158           159           160           161 
##   9747.554761  -3659.891392  -5007.798058   3372.156247    -31.954733 
##           162           163           164           165           166 
##   2470.811792  -6132.835774  -6974.137878   3925.610404  17165.982073 
##           167           168           169           170           171 
##   3408.231540   -617.676800  -2666.669383  -1327.160497   3366.115109 
##           172           173           174           175           176 
##   -451.589061  -8299.842648   2634.436753   4097.324253    398.919346 
##           177           178           179           180           181 
##   8523.326997  -9472.474810  -3701.138094 -10976.075158 -11476.740673 
##           182           183           184           185           186 
##    993.602458   9053.674905  -1664.968637   5692.896075   6321.006644 
##           187           188           189           190           191 
##  12923.890038   8196.383242  -4300.387899   2221.643756  10121.926025 
##           192           193           194           195           196 
##  -1892.390223  -2697.208610 -10536.232280  -6621.965132    973.865710 
##           197           198           199           200           201 
##  -5492.223160 -10054.789861   5125.124362  -3323.962047  -1967.113702 
##           202           203           204           205           206 
##  -1057.779788   6241.520540   9628.296034    321.723888   2666.270024 
##           207           208           209           210           211 
##   2838.182807   5523.484745  12571.713352  -5950.733407 -11559.756178 
##           212           213           214           215           216 
##  -5929.073378 -10848.631737  -5334.278659   1270.009073 -13265.465737 
##           217           218           219           220           221 
##  16136.883160   7556.356892   1273.476644  26432.393207  12263.719267 
##           222           223           224           225           226 
##   7067.468359  13755.740884  -4188.637639  -2016.464213   3502.041110 
##           227           228           229           230           231 
##     84.937957   2472.599650   8733.048409   5561.298234  -2171.808645 
##           232           233           234           235           236 
##  -2091.297833   9164.029546 -11769.947553  -7543.758040  -8800.516116 
##           237           238           239           240           241 
## -10359.621601   2821.090104   1096.612679  -8551.651468  -9242.397495 
##           242           243           244           245           246 
##   8843.866041  -8016.601148   2237.478275 -10550.984540  -4302.093761 
##           247           248           249           250           251 
##   1175.358781    755.388610 -12564.097998   3396.982567   1814.950910 
##           252           253           254           255           256 
##   3962.953896   1883.794279  -1413.336716  10885.418681  20621.638351 
##           257           258           259           260           261 
##   2925.859623  -4546.759571   3833.839384  -1973.023137   3458.776799 
##           262           263           264           265           266 
##  -5131.992183 -11171.935384  -5002.184084   -792.809275  -5458.774664 
##           267           268           269           270           271 
##   8509.860668  -4553.949962   3917.028121  -2382.592646   4155.420253 
##           272           273           274           275           276 
##    429.967535   7022.458484  -1698.598365  11738.247548  -4881.886021 
##           277           278           279           280           281 
##   1429.193886   -670.537873   7553.153927  -5362.271305  -3030.886463 
##           282           283           284           285           286 
## -11557.037062  -2952.463519  18375.734713   7486.842639   2429.606911 
##           287           288           289           290           291 
##   -935.685450    600.072893   6092.023618   6570.198442 -19090.825026 
##           292           293           294           295           296 
## -11432.027765  -8397.198115   9402.426523   2799.766382  -1453.920941 
##           297           298           299           300           301 
##  27129.610969   9757.238917   4580.564087   9193.404730   2522.250691 
##           302           303           304           305           306 
##  -1366.666073   7567.940321 -24630.300227  -3827.851874   -458.330905 
##           307           308           309           310           311 
##  -7246.915248  -4236.138289   2677.195560  -9449.184009  -3469.785001 
##           312           313           314           315           316 
##  -8418.400951   1348.303008  -3370.573429   1833.476175  -4302.431328 
##           317           318           319           320           321 
##  27230.070284  -1008.406218   3007.396932  10540.729306   5281.391464 
##           322           323           324           325           326 
##  32065.543414   4745.609019 -21301.256095   1474.517976    795.350355 
##           327           328           329           330           331 
##  -6776.331079  -2028.291330 -33553.790305    696.899216  -2484.797521 
##           332           333           334           335           336 
##   -268.286087  -3341.080402   3919.313458   -612.161237  -7127.259381 
##           337           338           339           340           341 
##  -3277.617502  -2347.614109  -7832.758762   3712.124610  -1523.029516 
##           342           343           344           345           346 
##  -1890.091839  -1145.779668     23.485471    324.579653  -1780.365240 
##           347           348           349           350           351 
##  -9608.778329 -13356.015986   2187.547952  -4457.075719  -3788.200042 
##           352           353           354           355           356 
##  -6107.123155   1630.840957   1253.077531   2611.236286  -3922.272154 
##           357           358           359           360           361 
##   -669.872526    518.755074   6847.564175     87.968865   -231.362231 
##           362           363           364           365           366 
##   2386.832518  -2955.406192  -1076.509019  -8940.885947  -4800.408592 
##           367           368           369           370           371 
##  -6375.640784  -5098.231218  -7391.380595   4890.834320    226.197603 
##           372           373           374           375           376 
##   6967.896226  -7812.941642  -2421.786253  -3543.781936  -2616.998926 
##           377           378           379           380           381 
## -12604.397793   1785.058871 -10763.879063   5588.584509   9204.024106 
##           382           383           384           385           386 
##   2959.963936  -2579.487863   1425.898466   6556.215066  11199.105938 
##           387           388           389           390           391 
##  -6050.973606  -5599.719052   -382.469927   8336.490850   1561.340519 
##           392           393           394           395           396 
##  10961.908893 -10171.991588   2508.266967    438.928645    287.648461 
##           397           398           399           400           401 
##   -928.724097   -834.418345 -14755.154629   8305.369140  -1418.461199 
##           402           403           404           405           406 
##  -1603.021275   6758.015755  -8175.875955  -1512.215018  -2739.427633 
##           407           408           409           410           411 
##  -6015.860779  -3034.731251  -4082.695678  -8908.790687   6005.459941 
##           412           413           414           415           416 
##   1488.217045  -7535.060883  -7833.986360  14099.025548   3639.261220 
##           417           418           419           420           421 
##   4295.314398  -8252.341854  -4935.784311  -2777.383433   2651.426325 
##           422           423           424           425           426 
## -14190.100227  -2927.317091  -9229.140839   2907.198378   6854.552088 
##           427           428           429           430           431 
##   6422.767910  -4167.418442  -4290.839172  -4881.573137  -1936.899015 
##           432           433           434           435           436 
##  -5857.308268  -6758.289312  -6065.793343  -1497.472649   -955.324684 
##           437           438           439           440           441 
##  -5087.704168   2477.006027   4718.224884  -5200.847304  -2292.031394 
##           442           443           444           445           446 
##   1443.482414  -3980.854637   2698.751678  -6729.278827 -12245.621173 
##           447           448           449           450           451 
##  -4614.969371   9548.046542  -2165.591409   4622.078411  -6020.921599 
##           452           453           454           455           456 
##  -1259.783043    245.942895   2883.125275 -12424.373025   3245.805393 
##           457           458           459           460           461 
##  -6839.569459   6399.026161   2864.899180   2347.285884  -4015.470470 
##           462           463           464           465           466 
##   1932.451745   -176.006906   1622.598260   -698.382166   3173.670837 
##           467           468           469           470           471 
##  -2826.636895   5625.818987  -7137.896235  -3137.454894  -2368.593447 
##           472           473           474           475           476 
##  -4820.140615   2853.191677   7643.640903  -6194.577191   1324.501797 
##           477           478           479           480           481 
##  -6343.279416  -2991.409639   1871.621077 -13077.950673  -9870.136317 
##           482           483           484           485           486 
##  -1296.101362    -77.004704  -1066.115409  -1449.351164  -9694.549934 
##           487           488           489           490           491 
##  11004.870245   6107.795819   7271.449910  -5608.437186   5209.319684 
##           492           493           494           495           496 
##   9118.584752   5853.433209 -13689.075139 -10741.248961  -3586.572844 
##           497           498           499           500           501 
##  -1242.903472   -660.169137  -7762.355579    493.419395   4165.277865 
##           502           503           504           505           506 
##   5371.694774    506.410165    -76.202567  -7396.689145    431.055291 
##           507           508           509           510           511 
##  -5190.430695   1702.210219  -1434.064021  -8294.128389   -719.368808 
##           512           513           514           515           516 
##  -2793.959645   -703.960622   1213.459317  -9621.322084  -7870.523773 
##           517           518           519           520           521 
##  24195.568327   9664.647610   5691.448063  -5538.545019   2611.233830 
##           522           523           524           525           526 
##  16825.389511  11235.990205 -24412.601710  -5252.873419  -3910.756517 
##           527           528           529           530           531 
##   4405.617569   -535.467155 -11279.857625   4244.325698  13749.540382 
##           532           533           534           535           536 
##  -5174.598944   4190.293209   5360.156103  -1998.985708  -4742.347313 
##           537           538           539           540           541 
##  -7260.847467  -2266.388165   8163.070153    -54.233613  -8321.971104 
##           542           543           544           545           546 
##   1658.374315   -762.131374    205.366368 -11192.837695 -11194.652524 
##           547           548           549           550           551 
##   1934.324412   6887.258231  -1456.082280    699.666129  -7862.448126 
##           552           553           554           555           556 
##   8441.387230    757.307343 -12097.761529   9038.172774   8505.047262 
##           557           558           559           560           561 
##    -79.219420   4673.963965  -3766.785829  13926.147350  21278.631337 
##           562           563           564           565           566 
##  -6741.804063  -9931.930337   6555.447092     -7.791896   3224.918008 
##           567           568           569           570           571 
##  -7611.308380 -17516.125145   6506.764267   6263.087454   1716.156471 
##           572           573           574           575           576 
##   2910.224437   1576.883690  -2359.271152  14531.828969  -9871.118281 
##           577           578           579           580           581 
##  -6437.775553   8536.769426   2662.344450  -6746.286578   7328.474213 
##           582           583           584           585           586 
##  -3997.881456  -2960.457040  15527.448042 -14713.828310   8254.633802 
##           587           588           589           590           591 
##   -123.368527  -6402.977338   -925.112714     85.231828 -10818.753365 
##           592           593           594           595           596 
##   1660.466465  -7285.798649   2944.327474   8733.425216  -7657.714184 
##           597           598           599           600           601 
##   5713.415376   2580.624386   6694.253740  -3368.739765   5980.928477 
##           602           603           604           605           606 
##  -8481.209979   2096.220283   1106.288642   2972.798962   1323.440986 
##           607           608           609           610           611 
##    224.094356  -5982.428340   7919.806609  -1357.458428  -2741.955213 
##           612           613           614           615           616 
##  -3611.960378  -8376.147332  11832.265561   4780.456406  -9480.871173 
##           617           618           619           620           621 
##  11484.871934   5881.118739  -5753.357180  26197.132412 -13045.000344 
##           622           623           624           625           626 
##  -6959.569931   2998.387058  -4322.376726 -10742.643735  11172.241303 
##           627           628           629           630           631 
## -21785.516501  -2515.965494   8576.987226  11015.658374  -1697.795611 
##           632           633           634           635           636 
##  33143.599308  -6798.740579   5527.164166   5202.318057  -2470.781151 
##           637           638           639           640           641 
##  -5535.541878  -2112.995060 -12595.477972  -2377.151030  -2015.520586 
##           642           643           644           645           646 
##  -2645.339923  -2977.848744   1700.588506   4311.430231  16835.145227 
##           647           648           649           650           651 
##  18387.312504    680.677535   4589.770521  10407.959401  19931.394274 
##           652           653           654           655           656 
##    496.675370 -28298.669066  -1507.748777  -2447.873254   1722.638594 
##           657           658           659           660           661 
##  -3335.408436 -10753.869748   1555.717780   4115.862595  -1123.439837 
##           662           663           664           665           666 
##  12918.903491   1225.698675   1679.335877 -11825.402920   1266.878639 
##           667           668           669           670           671 
##   1074.224747  -5279.330995  -7513.132493   1976.792519  -3804.566327 
##           672           673           674           675           676 
##   2586.266578  -3470.783542  -9423.888212  -8381.969955  -3046.804029 
##           677           678           679           680           681 
##    102.547168   2772.453599    631.548675  -3911.476746  -1890.480837 
##           682           683           684           685           686 
##  -1400.335358  -8325.194780   4573.629948  -2326.326468  -1481.577333 
##           687           688           689           690           691 
##    503.671347  10767.104009   9748.650074  10511.816773  -9783.524467 
##           692           693           694           695           696 
##  -3658.398817  -3238.017114   5777.305325 -10484.915504  -7997.899114 
##           697           698           699           700           701 
##  -8689.374910  -6345.382135  -4806.948586   3015.501528  -4474.718667 
##           702           703           704           705           706 
##  -1969.605529   4149.818436  31028.471017   9440.356895  23372.975596 
##           707           708           709           710           711 
##   1625.601509   8274.439598  22880.694021   6539.722179 -18216.075605 
##           712           713           714           715           716 
##   4802.562609  -5459.627816   -121.084794    457.726992 -17289.915127 
##           717           718           719           720           721 
##  -5300.256294   3295.334326  -3050.381878 -13017.076425   4233.205916 
##           722           723           724           725           726 
##  -5598.814725    697.052052  -3978.634938 -12492.875621   1315.164169 
##           727           728           729           730           731 
##  -1916.834396  -9827.023412  17214.706898   1733.733038  -2762.733721 
##           732           733           734           735           736 
##   5673.832355  -8667.840875   -766.020383   8095.919427 -15388.173136 
##           737           738           739           740           741 
##  -5957.343268   7359.144597  -4827.620357    115.320498   1783.185903 
##           742           743           744           745           746 
##  -1998.485987  -5211.348719   6366.745379  -6315.890611  22654.265804 
##           747           748           749           750           751 
##   7798.454868  -1971.191958  -7315.014371  23383.844465  -4307.249912 
##           752           753           754           755           756 
##   1375.857949 -14442.788241  56076.863618  26935.572893  15131.472284 
##           757           758           759           760           761 
## -10600.559673  10638.375885   7350.607201   5846.707239 -46352.255543 
##           762           763           764           765           766 
## -16179.437552    943.003152  -2539.127072  -3480.921654 122818.740096 
##           767           768           769           770           771 
##  19412.978388  43830.101859  22728.449390  12225.535090  15990.682226 
##           772           773           774           775           776 
##  25869.375772 -98657.800436  -6730.549490 -35816.772212   1710.944420 
##           777           778           779           780           781 
##  -1251.109438   3374.097011  -7431.480019  -1468.537869  -1968.418407 
##           782           783           784           785           786 
##   3401.117392  -7172.784718  -2260.607675   3895.385915   2247.854471 
##           787           788           789           790           791 
##  -2772.439766  -4062.490892   1720.926545   2839.165144    -61.115936 
##           792           793           794           795           796 
##  -6712.220491  -5797.772443  -1167.394381  -1282.447287  -7836.077448 
##           797           798           799           800           801 
##  -2379.417710  -3293.669396  -2692.045014  10697.623108   2228.887542 
##           802           803           804           805           806 
##   7070.889259   2923.052694  -5445.704002   8183.739532   9879.998063 
##           807           808           809           810           811 
## -10587.006400  -7394.234182  -7510.983464   2992.888879   4185.965358 
##           812           813           814           815           816 
##  -2272.485708 -14169.861333  -4129.640599   6238.445356   8225.033534 
##           817           818           819           820           821 
##  -9675.145042  -7761.605755  -9354.270712   9728.485617  -1235.351598 
##           822           823           824           825           826 
##  -4383.962149  -8462.143954   7852.668686   7902.664621   4973.436191 
##           827           828           829           830           831 
##  -3099.324033   -712.486619   2890.595363   4669.950460   1629.911507 
##           832           833           834           835           836 
##  -6687.834424   2087.303534   -397.911297   2752.953802   4142.552526 
##           837           838           839           840           841 
##  -1131.189989  -9331.654882   5670.137205  -4861.408751  -7582.398878 
##           842           843           844           845           846 
##   3011.060329 -13050.361373  -2836.913068  11610.036494   1306.077351 
##           847           848           849           850           851 
##    631.931302   -663.752979   4510.027660  12687.846338  -3676.230770 
##           852           853           854           855           856 
## -11559.210367  -1215.768813   5331.085801   7548.413247  -5829.161827 
##           857           858           859           860 
##  -5667.645583   1573.638980  -2264.368386   2499.552577 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17255.89  20100.98  24351.85  24070.02  26421.66  23756.27  24472.06  19707.67 
##        10        11        12        13        14        15        16        17 
##  19444.42  16789.13  17566.02  14297.82  14348.94  15013.11  16708.44  15029.70 
##        18        19        20        21        22        23        24        25 
##  16063.61  15437.60  22514.85  21599.53  21079.77  22968.39  22294.87  22946.94 
##        26        27        28        29        30        31        32        33 
##  24791.55  18724.39  20449.27  28280.85  28338.47  28011.62  25642.77  27044.18 
##        34        35        36        37        38        39        40        41 
##  30884.40  31232.57  32640.71  30153.14  34133.64  37339.37  34397.63  31210.85 
##        42        43        44        45        46        47        48        49 
##  30059.47  20645.92  28159.13  30593.83  31682.23  38515.31  38010.27  42669.04 
##        50        51        52        53        54        55        56        57 
##  46902.19  39605.47  34177.91  29204.26  22353.72  28639.11  25222.26  21522.58 
##        58        59        60        61        62        63        64        65 
##  25927.70  27186.33  27483.12  27894.64  23768.60  40347.70  42190.30  37439.75 
##        66        67        68        69        70        71        72        73 
##  41643.61  46555.64  57215.98  55231.21  40485.24  37992.94  41008.62  35312.95 
##        74        75        76        77        78        79        80        81 
##  30768.53  21478.81  24677.93  20606.64  22691.17  17590.45  19604.40  18831.49 
##        82        83        84        85        86        87        88        89 
##  17859.94  15930.29  17207.12  20812.52  25227.00  26216.14  26241.08  26857.36 
##        90        91        92        93        94        95        96        97 
##  30979.41  29810.82  30809.44  28875.30  28075.70  28443.58  28849.86  22432.04 
##        98        99       100       101       102       103       104       105 
##  25444.85  18480.46  17337.43  15379.60  15679.14  16356.02  20825.37  19909.55 
##       106       107       108       109       110       111       112       113 
##  23418.06  23208.18  24883.07  27757.71  25257.67  21703.77  21978.99  24623.31 
##       114       115       116       117       118       119       120       121 
##  35479.80  33673.98  35510.10  38506.21  40460.68  38150.81  32975.65  29319.74 
##       122       123       124       125       126       127       128       129 
##  31401.03  29682.25  30862.93  38457.08  38099.81  37162.04  34027.89  35807.77 
##       130       131       132       133       134       135       136       137 
##  41201.66  40642.37  31850.58  33114.44  36301.84  32715.14  31103.61  30189.29 
##       138       139       140       141       142       143       144       145 
##  26748.87  28162.25  27932.05  25620.06  27642.99  26268.76  19875.63  22903.91 
##       146       147       148       149       150       151       152       153 
##  20732.21  23707.12  24246.71  25835.92  26017.86  27670.80  28970.57  32000.47 
##       154       155       156       157       158       159       160       161 
##  27474.01  26737.52  24294.59  30184.30  41680.32  40011.80  37378.70  42395.24 
##       162       163       164       165       166       167       168       169 
##  43802.76  47216.12  42685.42  37996.10  43417.30  59707.34  61917.82  60333.10 
##       170       171       172       173       174       175       176       177 
##  57161.16  55561.60  58262.16  57286.99  49584.85  52406.25  56146.08  56182.24 
##       178       179       180       181       182       183       184       185 
##  63305.76  53815.14  50568.50  41384.03  32929.68  36435.33  46531.25  45987.68 
##       186       187       188       189       190       191       192       193 
##  51935.99  57676.68  68451.62  73730.53  67429.93  67623.22  74688.25  70367.92 
##       194       195       196       197       198       199       200       201 
##  65894.09  55145.97  49180.56  50603.79  46201.79  38376.45  44796.39  43025.11 
##       202       203       204       205       206       207       208       209 
##  42663.35  43141.34  49930.28  58812.85  58442.73  60166.25  61820.80  65609.14 
##       210       211       212       213       214       215       216       217 
##  75068.59  67157.33  55355.22  49968.06  40971.14  37931.13  41042.47  31070.12 
##       218       219       220       221       222       223       224       225 
##  48030.93  55346.24  56247.46  78995.85  86485.25  88486.97  96072.64  87030.32 
##       226       227       228       229       230       231       232       233 
##  81033.24  80615.49  77267.97  76430.09  81163.56  82526.81  76966.44  72182.97 
##       234       235       236       237       238       239       240       241 
##  77832.38  64490.19  56532.66  48489.34  40107.20  44295.96  46447.08  39902.68 
##       242       243       244       245       246       247       248       249 
##  33586.99  43861.74  38112.95  42045.70  34315.38  33022.21  36674.75  39496.53 
##       250       251       252       253       254       255       256       257 
##  30332.87  36266.48  40065.05  45255.92  47972.19  47465.15  57758.36  75242.43 
##       258       259       260       261       262       263       264       265 
##  75057.62  68373.30  69854.02  66077.65  67522.71  61285.08  50567.76  46598.09 
##       266       267       268       269       270       271       272       273 
##  46807.35  42917.00  51714.52  47990.40  52134.02  50252.01  54316.32  54612.11 
##       274       275       276       277       278       279       280       281 
##  60625.03  58261.04  67926.74  61856.09  62065.97  60416.27  66154.84  59890.03 
##       282       283       284       285       286       287       288       289 
##  56456.47  46016.61  44414.55  61633.87  67159.82  67568.97  64988.50  64076.55 
##       290       291       292       293       294       295       296       297 
##  68074.52  71981.83  52992.60  43102.06  37117.57  47431.23  50670.64  49785.25 
##       298       299       300       301       302       303       304       305 
##  73963.48  79904.44  80571.60  85180.61  83380.52  78414.49  81878.73  56796.28 
##       306       307       308       309       310       311       312       313 
##  53060.19  52740.20  46535.00  43746.52  47347.18  39904.93  38627.97  33193.55 
##       314       315       316       317       318       319       320       321 
##  36975.29  36157.24  39985.86  37971.79  63738.98  61581.75  63204.13  71196.32 
##       322       323       324       325       326       327       328       329 
##  73581.89  99044.68  97423.54  73271.62  72070.36  70428.90  62386.58  59510.93 
##       330       331       332       333       334       335       336       337 
##  29481.53  33166.37  33605.57  35923.79  35265.12  41027.88  42102.69  37353.76 
##       338       339       340       341       342       343       344       345 
##  36568.76  36695.33  32017.73  38012.32  38675.23  38933.49  39808.66  41593.28 
##       346       347       348       349       350       351       352       353 
##  43413.94  43165.78  36115.59  26690.31  32031.08  30892.91  30483.27  28101.44 
##       354       355       356       357       358       359       360       361 
##  32776.92  36528.48  40988.84  39179.16  40438.53  42575.44  49965.32  50515.51 
##       362       363       364       365       366       367       368       369 
##  50717.02  53178.41  50663.65  50108.60  42759.12  39957.93  36137.66  33917.95 
##       370       371       372       373       374       375       376       377 
##  29978.59  37261.23  39546.53  47426.37  41402.36  40849.92  39388.28  38921.40 
##       378       379       380       381       382       383       384       385 
##  29795.66  34390.45  27447.13  35660.55  45986.18  49549.06  47823.67  49813.93 
##       386       387       388       389       390       391       392       393 
##  56029.61  65508.26  58724.43  53196.61  52925.51  60299.80  60822.81  69485.28 
##       394       395       396       397       398       399       400       401 
##  58598.73  60164.50  59724.92  59209.15  57697.13  56459.58  43227.63  51807.18 
##       402       403       404       405       406       407       408       409 
##  50808.31  49775.27  56172.02  48719.79  48031.43  46359.29  42039.59  40871.12 
##       410       411       412       413       414       415       416       417 
##  38936.36  33034.68  40901.93  43826.20  38502.27  33593.97  48455.17  52297.26 
##       418       419       420       421       422       423       424       425 
##  56223.77  48698.21  45024.10  43701.00  47284.96  35712.17  35441.57  29704.37 
##       426       427       428       429       430       431       432       433 
##  35290.31  43612.09  50499.42  47267.12  44337.86  41265.18  41153.45  37633.72 
##       434       435       436       437       438       439       440       441 
##  33774.79  31010.76  32585.75  34433.85  32439.85  37302.63  43503.85  40258.46 
##       442       443       444       445       446       447       448       449 
##  39964.66  42969.00  40856.53  44843.28  40093.48  31131.97  29970.24  41319.31 
##       450       451       452       453       454       455       456       457 
##  41001.06  46648.35  42287.50  42636.91  44256.30  47971.94  37853.19  42699.14 
##       458       459       460       461       462       463       464       465 
##  38125.55  45689.39  49207.00  51825.76  48557.55  50896.72  51098.12  52843.95 
##       466       467       468       469       470       471       472       473 
##  52341.90  55283.64  52613.75  57661.47  50926.03  48538.59  47125.71  43752.38 
##       474       475       476       477       478       479       480       481 
##  47505.93  54964.15  49394.93  51096.99  45889.41  44269.52  47100.52  36521.99 
##       482       483       484       485       486       487       488       489 
##  30087.96  31956.00  34650.83  36139.78  37104.98  30750.13  43271.78  49927.41 
##       490       491       492       493       494       495       496       497 
##  56753.01  51468.11  56297.84  63926.28  67735.08  54000.82  44585.14  42611.47 
##       498       499       500       501       502       503       504       505 
##  42934.45  43725.07  38215.58  40612.86  45910.73  51588.45  52297.63  52408.12 
##       506       507       508       509       510       511       512       513 
##  46114.37  47453.43  43715.22  46468.78  46134.70  39854.80  40985.10  40160.82 
##       514       515       516       517       518       519       520       521 
##  41265.68  43903.89  36748.95  32031.57  55904.78  64059.84  67710.26  61093.91 
##       522       523       524       525       526       527       528       529 
##  62432.47  76008.72  82980.60  57948.16  52821.76  49518.38  53894.32  53401.00 
##       530       531       532       533       534       535       536       537 
##  43591.39  48579.75  61231.46  55756.14  59151.42  63136.41  60191.06  55225.28 
##       538       539       540       541       542       543       544       545 
##  48692.10  47348.93  55280.52  55031.11  47596.34  49818.42  49645.21  50338.55 
##       546       547       548       549       550       551       552       553 
##  40994.08  32835.53  37174.31  45285.23  45082.33  46787.02  40801.04  49807.69 
##       554       555       556       557       558       559       560       561 
##  50962.19  40748.54  50282.81  58140.08  57505.46  61100.64  56870.85  68623.08 
##       562       563       564       565       566       567       568       569 
##  85299.95  75397.93  63969.55  68385.65  66511.37  67697.17  59273.13  43273.52 
##       570       571       572       573       574       575       576       577 
##  50277.20  56178.13  57360.06  59434.12  60080.70  57209.17  69447.12  58828.06 
##       578       579       580       581       582       583       584       585 
##  52555.52  60151.66  61654.57  54753.53  61015.60  56594.89  53641.55  67201.97 
##       586       587       588       589       590       591       592       593 
##  52640.94  59979.94  59072.98  52799.68  52105.34  52381.18  43103.68  45898.51 
##       594       595       596       597       598       599       600       601 
##  40528.82  44771.57  53528.57  46864.58  52719.38  55095.46  60760.45  56921.36 
##       602       603       604       605       606       607       608       609 
##  61731.64  53306.35  55185.00  55960.77  58267.27  58840.91  58382.00  52563.62 
##       610       611       612       613       614       615       616       617 
##  59620.17  57681.67  54780.96  51489.43  44457.45  55959.40  59844.01  50785.99 
##       618       619       620       621       622       623       624       625 
##  61180.45  65362.36  58856.87  81068.29  66201.86  58536.76  60538.23  55894.93 
##       626       627       628       629       630       631       632       633 
##  46237.33  56936.95  37507.39  37367.73  46929.06  57404.08  55450.11  84158.17 
##       634       635       636       637       638       639       640       641 
##  74351.55  76550.68  78186.78  72916.97  65641.57  62278.34  50192.15  48561.66 
##       642       643       644       645       646       647       648       649 
##  47454.05  45937.42  44323.27  46998.14  51612.14  66571.97  80985.61  78111.09 
##       650       651       652       653       654       655       656       657 
##  79014.18  84881.32  98316.04  93078.53  63370.61  60824.30  57780.93  58764.84 
##       658       659       660       661       662       663       664       665 
##  55208.44  45628.28  48010.85  52325.44  51518.24  63071.44  62949.24  63238.55 
##       666       667       668       669       670       671       672       673 
##  51702.55  53061.06  54078.76  49420.99  43405.21  46437.85  44038.45  47522.64 
##       674       675       676       677       678       679       680       681 
##  45276.75  38119.68  32781.66  32779.17  35526.12  40254.59  42513.33  40519.34 
##       682       683       684       685       686       687       688       689 
##  40542.91  40991.34  35337.94  41662.61  41160.43  41459.47  43453.47  54153.21 
##       690       691       692       693       694       695       696       697 
##  62604.18  70647.38  59952.26  55963.02  52847.69  57997.92  48298.04  42001.80 
##       698       699       700       701       702       703       704       705 
##  35902.10  32623.66  31104.78  36607.29  34872.18  35544.32  41472.81  70110.79 
##       706       707       708       709       710       711       712       713 
##  76264.74  93798.68  90120.70  92714.02 107727.85 106569.36  83948.29  84295.34 
##       714       715       716       717       718       719       720       721 
##  75640.23  72745.13  70723.20  53465.97  48867.81  52357.24  49863.93  38987.37 
##       722       723       724       725       726       727       728       729 
##  44551.10  40825.23  43068.63  40945.45  31659.84  35607.55  36232.31  29872.72 
##       730       731       732       733       734       735       736       737 
##  47926.55  50172.45  48207.88  53857.41  46269.88  46544.22  54519.46  40981.49 
##       738       739       740       741       742       743       744       745 
##  37396.28  45890.91  42667.97  44169.39  46935.91  46049.78  42471.68  49455.03 
##       746       747       748       749       750       751       752       753 
##  44480.02  65425.83  70741.91  66854.30  58796.01  78559.39  71639.14  70559.22 
##       754       755       756       757       758       759       760       761 
##  55808.14 104489.57 121546.53 126131.85 107672.48 110098.82 109346.86 107377.68 
##       762       763       764       765       766       767       768       769 
##  60093.29  45156.28  47063.98  45689.64  43667.83 152152.31 156585.61 181769.69 
##       770       771       772       773       774       775       776       777 
## 185333.32 179275.89 177274.91 184151.51  81452.12  72048.92  38450.77  41880.97 
##       778       779       780       781       782       783       784       785 
##  42289.62  46683.77  41087.11  41406.85  41249.60  45799.50  40541.04  40238.76 
##       786       787       788       789       790       791       792       793 
##  45348.57  48370.87  46626.78  43978.22  46714.69  50079.54  50485.08  45033.20 
##       794       795       796       797       798       799       800       801 
##  41072.39  41656.88  42066.65  36703.56  36785.24  36058.47  35949.23  47541.97 
##       802       803       804       805       806       807       808       809 
##  50268.97  56876.09  59022.85  53591.55  60747.86  68475.43  57354.95  50434.70 
##       810       811       812       813       814       815       816       817 
##  44291.97  48098.89  52463.49  50635.72  38654.78  36960.70  44532.40  52876.00 
##       818       819       820       821       822       823       824       825 
##  44533.89  38922.27  32633.51  43801.64  43979.96  41387.14  35563.90  44722.19 
##       826       827       828       829       830       831       832       833 
##  52760.28  57219.90  54065.92  53396.26  55956.91  59745.37  60398.69  53708.27 
##       834       835       836       837       838       839       840       841 
##  55528.05  54947.19  57190.59  60361.90  58526.65  49766.29  55214.55  50777.26 
##       842       843       844       845       846       847       848       849 
##  44528.65  48321.36  37611.77  37178.68  49411.64  51095.50  51976.90  51615.26 
##       850       851       852       853       854       855       856       857 
##  55815.87  66621.23  61768.92  50652.05  49976.91  55102.44  61512.16  55429.79 
##       858       859       860 
##  50261.36  52070.80  50300.02 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8088
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original      bias    std. error
## t1*    5.312009   0.8284048    4.117924
## t2* 2736.418563 164.4655089  894.598139
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.188825       5.259271   14.00103
## 2    lag_depvar 1661.886281    2776.360918 4561.47102

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Sep  8 00:59:12 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Sep  8 00:59:22 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Sep  8 00:59:31 2025
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##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Sep  8 00:59:51 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Sep  8 01:00:01 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Sep  8 01:00:11 2025
##  =-=-=-=-=
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##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_25<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2025",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2020",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_25 %>% 
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
Item 2025 2024 2023 2022 2021 2020
Agua 7.180125 6.993667 5.195333 5.410333 5.849167 9.93775
Comida 223.381125 326.890000 366.009167 312.386750 317.896583 392.93367
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
Electricidad 55.862500 83.582750 38.104750 47.072333 29.523000 20.60458
Enceres 3.335000 23.989000 18.259750 24.219750 14.801167 39.01200
Farmacia 0.000000 0.000000 10.704083 2.835000 13.996083 14.03675
Gas/Bencina 23.106250 44.292667 42.636000 45.575000 13.583667 17.25833
Diosi 16.561125 33.319583 55.804250 31.180667 52.687833 37.12133
donaciones/regalos 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
Electrodomésticos/ Mantención casa 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
VTR 16.497500 18.326667 12.829167 25.156667 19.086917 19.11375
Netflix 0.000000 1.391417 8.713833 7.151583 7.028750 8.24725
Otros 0.000000 76.164000 5.481667 5.000000 0.000000 0.00000
Total 345.923625 614.949750 563.738000 505.988083 474.453167 558.26542
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")

tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2808, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)
    

fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
    dplyr::rename(ds = date3, y = value) %>%
    dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
    dplyr::mutate(unique_id = "serie_1")%>%
    dplyr::select(unique_id, ds, y)

# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
  uf_timegpt,
  h = 298,               # 298 días a pronosticar
  freq = "D",            # Frecuencia diaria
  add_history = TRUE,     # Incluir datos históricos en el output
  level = c(80,95),
  model=  "timegpt-1-long-horizon", 
  clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>% 
    mutate(ds = as.Date(ds))

timegpt_fcst <- timegpt_fcst %>% 
    mutate(ds = as.Date(ds))

# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
    uf_timegpt %>% mutate(type = "Histórico"),
    timegpt_fcst %>% mutate(type = "Pronóstico")
)

# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
    # Intervalo de confianza del 95%
    geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`), 
                fill = "#4B9CD3", alpha = 0.2) +
    # Intervalo de confianza del 80%
    geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`), 
                fill = "#4B9CD3", alpha = 0.3) +
    # Línea histórica
    geom_line(data = filter(full_data, type == "Histórico"), 
              aes(color = "Histórico"), size = 1) +
    # Línea de pronóstico
    geom_line(data = filter(full_data, type == "Pronóstico"), 
              aes(color = "Pronóstico"), size = 1) +
    # Línea vertical separadora
    geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds), 
               linetype = "dashed", color = "red", size = 0.8) +
    # Configuración del eje x
    scale_x_date(
        date_breaks = "3 months",  # Reduce la frecuencia de las etiquetas
        date_labels = "%b %Y",  # Formato de etiquetas (mes y año)
    ) +
    # Configuración del eje y
    scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
    # Configuración de colores
    scale_color_manual(
        name = "Leyenda",
        values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
    ) +
    # Títulos y subtítulos
    labs(
        title = "Pronóstico de Serie Temporal con TimeGPT",
        subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
        x = "Fecha",
        y = "Valor",
        color = "Leyenda"
    ) +
    # Tema y estilos
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.position = "bottom",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
    )
## Warning: Removed 2808 rows containing missing values or values outside the scale range
## (`geom_line()`).

library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
## 
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
## 
##     %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
##     flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
## 
##     invoke
## The following object is masked from 'package:data.table':
## 
##     :=
  model <- prophet(
  cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
  # Trend flexibility
  growth = "linear",
  changepoint.prior.scale = 0.05,  # Reduced for smoother trend
  n.changepoints = 50,  # Increased from default 25
  
  # Seasonality
  yearly.seasonality = TRUE,
  weekly.seasonality = TRUE,
  daily.seasonality = FALSE,  # Disabled for daily data
  seasonality.mode = "additive",
  seasonality.prior.scale = 15,  # Increased to capture stronger seasonality
  
  # Holidays (if applicable)
  # holidays = generated_holidays  # Create with add_country_holidays()
  
  # Uncertainty intervals
  interval.width = 0.95,
  uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)

ggplot(forecast, aes(x = ds, y = yhat)) +
  geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper), 
              fill = "#9ecae1", alpha = 0.4) +
  geom_line(color = "#08519c", linewidth = 0.8) +
  geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
  scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
  scale_y_continuous(labels = scales::comma) +
  labs(title = "Valores predichos (95%IC)",
      # subtitle = "March 10, 2025 - May 7, 2025",
       x = "Fecha",
       y = "Valor",
      # caption = "Source: Prophet Forecast Model"
      ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "gray50"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    plot.caption = element_text(color = "gray30")
  )

La proyección de la UF a 298 días más 2025-09-09 00:04:58 sería de: 26.848 pesos// Percentil 95% más alto proyectado: 35.188,75

Según TimeGPT: La proyección de la UF a 298 días más 2026-07-04 sería de: 40.201,26 pesos// Percentil 80% más alto proyectado: 42.239,44 pesos// Percentil 95% más alto proyectado: 42.556,3

Según prophet: La proyección de la UF a 298 días más 2026-07-04 sería de: 42.517 pesos// Percentil 95% más alto proyectado: 49.912

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 26460.95 26321.73
Lo.80 26594.36 26486.94
Point.Forecast 26848.21 26799.01
Hi.80 31608.29 32186.25
Hi.95 34460.82 35038.08


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.4483  1029.8941
## s.e.  0.1022    39.3997
## 
## sigma^2 = 38941:  log likelihood = -528.7
## AIC=1063.4   AICc=1063.72   BIC=1070.51
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.4536   599.6322  13.2153
## s.e.  0.1033   315.9027   9.6172
## 
## sigma^2 = 38484:  log likelihood = -527.72
## AIC=1063.45   AICc=1063.99   BIC=1072.92
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 623.5974 597.1924 553.2689
Lo.80 772.9295 746.9591 629.9390
Point.Forecast 1055.0244 1029.8753 804.9503
Hi.80 1337.1193 1312.7914 1160.2933
Hi.95 1486.4514 1462.5581 1408.0902


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)

Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))

Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")

Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))

Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
  mutate(value = Tami + Andrés) %>%
  rename(ds = mes_ano, y = value) %>%
  mutate(#ds= format(ds, "%Y-%m"),
         unique_id = "1") %>% #it is only one series
  select(unique_id, ds, y)

#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
  dplyr::rename(ds = date3, y = value) %>%
  dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
  dplyr::mutate(unique_id = "serie_1")%>%
  dplyr::select(unique_id, ds, y) %>%
  mutate(ds = ymd(ds)) %>%  # Convert 'ds' to Date
  mutate(month = month(ds), year = year(ds)) %>%  # Extract month and year
  group_by(month, year) %>%  # Group by month and year
  summarise(average_y = mean(y))%>%  # Calculate average y
  mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
  ungroup()%>%
  select(ds, uf=average_y)

Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |> 
  dplyr::left_join(uf_timegpt_my, by=c("ds"="ds")) 

#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
  Gastos_casa_mensual_2022_timegpt_ex,
  h = 12,
  freq = "M",  # Monthly frequency
  add_history = TRUE,
  level = c(80, 95),
  model = "timegpt-1",#"timegpt-1-long-horizon",
  clean_ex_first = TRUE
)

# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

# Combine historical and forecast data
full_data_gastos <- bind_rows(
  Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
  gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)

full_data_gastos |> 
  dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |> 
# Visualize results
ggplot(aes(x = ds, y = y)) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
              fill = "#4B9CD3", alpha = 0.2) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
              fill = "#4B9CD3", alpha = 0.3) +
  geom_line(aes(color = type), linewidth = 1.5) +
  geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
             linetype = "dashed", color = "red", linewidth = 0.8) +
  scale_x_date(
    date_breaks = "3 months",
    date_labels = "%b %Y"
  ) +
  scale_y_continuous(
    name = "Gastos Totales",
    labels = scales::comma,
    breaks = pretty(full_data_gastos$y, n = 10),
    expand = expansion(mult = c(0.05, 0.05))
  ) +
  scale_color_manual(
    name = "Leyenda",
    values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
  ) +
  labs(
    title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
    subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
    x = "Fecha",
    y = "Gastos Totales",
    color = "Leyenda"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.title.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    legend.position = "bottom",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## system code page: 65001
## 
## time zone: UTC
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] prophet_1.0         rlang_1.1.6         Rcpp_1.1.0         
##  [4] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [7] Boom_0.9.15         scales_1.4.0        ggiraph_0.9.0      
## [10] tidytext_0.4.3      DT_0.34.0           janitor_2.2.1      
## [13] autoplotly_0.1.4    rvest_1.0.5         plotly_4.11.0      
## [16] xts_0.14.1          forecast_8.24.0     wordcloud_2.6      
## [19] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-16          
## [22] NLP_0.3-2           tsibble_1.1.6       lubridate_1.9.4    
## [25] forcats_1.0.0       dplyr_1.1.4         purrr_1.1.0        
## [28] tidyr_1.3.1         tibble_3.3.0        tidyverse_2.0.0    
## [31] gsynth_1.2.1        sjPlot_2.9.0        lattice_0.22-6     
## [34] GGally_2.4.0        ggplot2_3.5.2       gridExtra_2.3      
## [37] plotrix_3.8-4       sparklyr_1.9.1      httr_1.4.7         
## [40] readxl_1.4.5        zoo_1.8-14          stringr_1.5.1      
## [43] stringi_1.8.7       DataExplorer_0.8.4  data.table_1.17.8  
## [46] reshape2_1.4.4      fUnitRoots_4040.81  plyr_1.8.9         
## [49] readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-9          cellranger_1.1.0      httr2_1.2.1          
##   [4] lifecycle_1.0.4       StanHeaders_2.32.10   doParallel_1.0.17    
##   [7] globals_0.18.0        vroom_1.6.5           MASS_7.3-60.2        
##  [10] crosstalk_1.2.2       magrittr_2.0.3        sass_0.4.10          
##  [13] rmarkdown_2.29        jquerylib_0.1.4       yaml_2.3.10          
##  [16] fracdiff_1.5-3        doRNG_1.8.6.2         askpass_1.2.1        
##  [19] pkgbuild_1.4.8        DBI_1.2.3             abind_1.4-8          
##  [22] quadprog_1.5-8        nnet_7.3-19           rappdirs_0.3.3       
##  [25] sandwich_3.1-1        inline_0.3.21         data.tree_1.2.0      
##  [28] tokenizers_0.3.0      listenv_0.9.1         anytime_0.3.12       
##  [31] spatial_7.3-17        parallelly_1.45.1     codetools_0.2-20     
##  [34] xml2_1.4.0            tidyselect_1.2.1      farver_2.1.2         
##  [37] urca_1.3-4            its.analysis_1.6.0    matrixStats_1.5.0    
##  [40] stats4_4.4.0          jsonlite_2.0.0        ellipsis_0.3.2       
##  [43] Formula_1.2-5         iterators_1.0.14      systemfonts_1.2.3    
##  [46] foreach_1.5.2         tools_4.4.0           glue_1.8.0           
##  [49] xfun_0.53             TTR_0.24.4            ggfortify_0.4.19     
##  [52] loo_2.8.0             withr_3.0.2           timeSeries_4041.111  
##  [55] fastmap_1.2.0         boot_1.3-30           openssl_2.3.3        
##  [58] caTools_1.18.3        digest_0.6.37         timechange_0.3.0     
##  [61] R6_2.6.1              lfe_3.1.1             colorspace_2.1-1     
##  [64] networkD3_0.4.1       gtools_3.9.5          generics_0.1.4       
##  [67] htmlwidgets_1.6.4     ggstats_0.10.0        pkgconfig_2.0.3      
##  [70] gtable_0.3.6          timeDate_4041.110     lmtest_0.9-40        
##  [73] S7_0.2.0              selectr_0.4-2         janeaustenr_1.0.0    
##  [76] htmltools_0.5.8.1     carData_3.0-5         tseries_0.10-58      
##  [79] snakecase_0.11.1      knitr_1.50            rstudioapi_0.17.1    
##  [82] tzdb_0.5.0            uuid_1.2-1            nlme_3.1-164         
##  [85] curl_7.0.0            cachem_1.1.0          KernSmooth_2.23-22   
##  [88] parallel_4.4.0        fBasics_4041.97       pillar_1.11.0        
##  [91] vctrs_0.6.5           gplots_3.2.0          slam_0.1-55          
##  [94] car_3.1-3             dbplyr_2.5.0          xtable_1.8-4         
##  [97] evaluate_1.0.5        mvtnorm_1.3-3         cli_3.6.5            
## [100] compiler_4.4.0        crayon_1.5.3          rngtools_1.5.2       
## [103] future.apply_1.20.0   labeling_0.4.3        rstan_2.32.7         
## [106] QuickJSR_1.8.0        viridisLite_0.4.2     assertthat_0.2.1     
## [109] lazyeval_0.2.2        Matrix_1.7-0          hms_1.1.3            
## [112] bit64_4.6.0-1         future_1.67.0         nixtlar_0.6.2        
## [115] extraDistr_1.10.0     igraph_2.1.4          RcppParallel_5.1.11-1
## [118] bslib_0.9.0           quantmod_0.4.28       bit_4.6.0
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))